Spiking Neural Networks for Energy-Efficient Acoustic Emission-Based Monitoring
Acoustic emission (AE) is one of the most effective nondestructive testing (NDT) techniques for the identification and characterization of stress waves originated at the uprising of acoustic-related defects (e.g., cracks). To this end, the estimation of the time of arrival (ToA) is crucial. In this...
Saved in:
Main Authors: | Federica Zonzini, Wenliang Xiang, Luca de Marchi |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Open Journal of Instrumentation and Measurement |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10734354/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
APPLICATION AND RESEARCH OF ACOUSTIC EMISSION TECHNOLOGY IN DYNAMIC MONITORING OF WIND TOWER
by: ZHANG PengLin, et al.
Published: (2016-01-01) -
Defining quantitative parameters and coordinates of the defect signal by acoustic-emission control of cylindrical tanks
by: A. A. Kuznetsov, et al.
Published: (2024-02-01) -
Enhancing navigation performance in unknown environments using spiking neural networks and reinforcement learning with asymptotic gradient method
by: Xiaode Liu, et al.
Published: (2025-01-01) -
SPICE-Level Demonstration of Unsupervised Learning With Spintronic Synapses in Spiking Neural Networks
by: Salah Daddinounou, et al.
Published: (2025-01-01) -
Pitting Detection and Characterization From Ultrasound Timelapse Images Using Convolutional Neural Networks
by: Magnus Wangensteen, et al.
Published: (2024-01-01)